In the example, the use of the demonstrative resembles a definite article, but it seems that none of the situations observed for Mandarin applies here.

the demonstrative in (3) must have other functions, probably non-deictic and more pragmatic ones, like how some other lexical items may have developed (e.g.


Based on the above observations, this study aims at attesting the non-deictic usage of demonstratives in contemporary Hong Kong Cantonese with a corpus-based approach.

we analyse and compare the use of demonstratives in three Cantonese corpora, covering different speech types, to illuminate the relationship between the functions of demonstratives and speech contexts.


Based on the actual frequencies from the samples, a statistically significant difference was found for the proportion of demonstratives among the three corpora (χ 2 =47.15, df=2, p<0.05).

it is HKVerCom which shows a significantly lower proportion of demonstratives than the other two corpora, which is somewhat counter-intuitive.


This, in effect, even counter-balances the differences in the normalised frequencies of 嗰 go3 across the three corpora.

despite the much lower frequencies of 嗰 go2 in public discussions and interpreted speeches, they actually use more 嗰個 go2go3 than face-to-face conversations.


As what was 'said' is in the form of a question, if the question functions as an information-seeking question, the verb 問 'ask' would be used instead.

the verb 說 'say' reveals that the following question is a rhetorical question which is not used to seek information.


Transliteration can be considered as a subtask of machine translation, when we need to translate source graphemes into target phonemes.

an alignment model needs to be constructed first, and the translation model is built on the basis of the alignments.


Given the grapheme sequence of a word and a phoneme sequence corresponding to its pronunci-ation, an alignment strategy consists of associating each grapheme with its corresponding phoneme, often of length 1.

the grapheme and phoneme sequences are aligned to form joint grapheme-phoneme units, which are called graphone.


By EIs, we mean indefinites that 2 "signal ignorance on the part of the speaker, thereby conveying information about her epistemic state" (Alonso-Ovalle and Menéndez-Benito, 2015:2).

eIs are used with speaker's failure in identification of the referent.


17 Potts' (2005Potts' ( , 2015 sense, defined as follows: (19) Meaning p is a CI of phrase S iff a. p is a conventional (encoded) property of a lexical item or construction in S; b. p is entailed by S; and, c. p's truth or falsity has no effect on the atissue content of S. (Potts 2015:(17)) To see how EIs and RIs fit into the picture depicted in (19a) and (19b), the ignorance component in EIs is encoded by m-zi.

the ignorance component is entailed in EIs.


With regard to (19c), the logical independence between at-issue content and CI can be verified by "yes, but…" test in (20)-(21) (Karttunen and Peters, 1979;Potts, 2005).

the response "yes, but…" disputes only the CI content, but not the at-issue content.the "yes" in B's utterance agrees with the truth value of the atissue content (i.e.


The jauconstruction shows Definiteness Effect (Huang, 1987).

only indefinite NPs, but not definite NPs, can appear after an existential marker jau in jau-construction.


(32) Semantics of m-zi and WH (first version) ⟦m-zii / WHi ⟧ g = g(i) ∈ Dcf (33) A special compositional rule for EIs and RIs ⟦m-zii / WHi XP⟧ = ⟦m-zii / WHi ⟧ (⟦XP⟧ F ) For RIs, we suggest that the phonetic features of WH is acquired during derivation.

it copies whatever phonetic features of its complement at the PF component (resulting in reduplication, see §5.2 for complications in formation of RIs).


We suggested that the phonetic features of WH are acquired during derivation.

they copy the features of its complement.


A vast array of research on L2 writers' texts to date has been mostly dependent on the cognitive behavior models of the second language writing process to lay the theoretical groundwork (see Lee, 2017).

crosslinguistic influences have been the focus of ongoing research endeavors in second language writing (Cumming, 1990).


4 The three sub-corpora include native scholars' L1 English abstracts (NE), Korean scholars' L2 English abstracts of which articles were written in English (QE, meaning quasi-L2 English), and finally Korean scholars' L2 English abstracts of which articles were produced in L1 Korean (KE).

by the speculation that Korean scholars' Korean articles may have served as source texts, Korean scholars' L2 English abstracts have been separately categorized into two different groups to prevent such source-text effects, if any.


Both QE and KE corpora had lower STTR values than the NE corpus.

the value of STTR in KE corpus was far lower than that of QE.


• Third tone sandhi rule: T3 (213) → T2 (35)/__ T3 (213) lau 213-pan 213 → lau 35-pan 213 "boss" It has been shown that Cantonese speakers acoustically differed from Beijing Mandarin speakers in their application of two Mandarin tone sandhi rules (Chen et al., 2017).

cantonese speakers preferred to neutralize the sandhi Tone 3 more with Tone 2 in isolation which indicated a more categorical application of the third tone sandhi rule.


The first part was an auditory and visual explanation of tone sandhi rules, as well as an auditory-only practice.

odd trials were naturally produced sandhi stimuli and even trials were the synthesized stimuli without tone sandhi.


After anonymization, the raters were instructed to evaluate the realization of tone sandhi and avoid the effects of consonants and vowels.

the first syllable of each disyllabic word was rated on a 101point scale, which evaluated the degree of application of tone sandhi as well as the naturalness of tone production.


In other words, the first syllable of each disyllabic word was rated on a 101point scale, which evaluated the degree of application of tone sandhi as well as the naturalness of tone production.

100 point referred to full application of tone sandhi rules in terms of native listeners' intuition which was close to native-like level, whereas 0 point indicated non-application of tone sandhi rules which was pronounced with a heavy non-native accent.


Consistent with native speakers, as Beijing speakers obtained 92.23 for real words and 91.42 for wug words with significant difference (p < .001), Cantonese speakers of TG and CG both significantly differed in producing real words and wug words.

mean scores of TG were 71.83 and 64.91 respectively for real and wug words (p < .001) in the pre-test.


Another phenomenon is that Cantonese speakers probably produce two isolated Tone 3 without sandhi.

they imitate the two monosyllables rather than produce them into a disyllabic word.


The acoustic study (Chen et al., 2017) on Mandarin production by Cantonese speakers suggests that Cantonese speakers are able to apply Mandarin third tone sandhi rule with a computational mechanism.

cantonese speakers tend to apply this rule on real words and wug words in the same way.


The possibility that Cantonese speakers apply the half-third sandhi rule better in post-test is realized by their prior knowledge of L1 tonal system.

cantonese speakers tend to map Mandarin Tone 3 (214) into cantonese T4 (21) according to the perceptual assimilation of Mandarin tones.


We use the dictionaries to correct errors directly.

we search errors in text and replace them with counter-  parts in the dictionaries.


The specific meaning is shown in Table 1: After the word segmentation and dependency of corpus are determined, the extraction algorithm of dependency relation triples is as follows: Step 1: generate a dictionary that holds the dependency child nodes for each word in the sentence.

find the child node corresponding to each word by "head" and record the dependency in the dictionary.


Using the indices and outdices on a derivation tree, it is possible to infer memory usage of the parser when conjecturing that tree.


Additionally, we also try out pre-processing before OT to see how it affects the performance of MSA.

we apply length normalization (+LN), and length normalization and mean centering together (+LN&MC) before OT.


First, our deep learning paradigm performed better than the MT+SVM method (traditional paradigm).

the parameter-sharing CNN model outperformed MT+SVM model by 4.3% (57.3% vs. 53.0%).


As shown in Figure 1, Pearson's correlation test showed that the number of Chinese characters in each CC family and the number of CC families were negatively correlated (r = -0.711, p < 0.001).

the larger a family was, the fewer such CC families were there in Korean language.


According to the studies emphasising on the requirement of large vocabulary to understand a text, Laufer (1992) and Nation (2006) showed that reading comprehension is correlated with the ration of vocabulary (referred to as text covering ratio) known by a reader with regard to a certain text.

if a text is represented only by a certain known vocabulary, then understanding that text becomes easy.


A backward LM is similar to a forward LM except that it is provided with the reversed input of a sequence.

this model predicts the previous token t k considering the future sequence (t k+1 , ..., t N ).


In addition, their corpora have high scores in fluency and meaning preservation based on a manual evaluation.

their core vocabulary can cover a wide range of expressions in Japanese.


To overcome the data sparseness problem, deepneural-network-based methods have attracted much attention because of their ability to use dense feature representations (Socher et al., 2011;Socher et al., 2013;Kim, 2014;Kalchbrenner et al., 2014;Tai et al., 2015;Zhang and Komachi, 2015).

tree-structured approaches called recursive neural networks (RvNNs) have been shown to perform well in sentiment classification tasks (Socher et al., 2011;Socher et al., 2013;Kim, 2014;Tai et al., 2015).


While most computer science research in this field has focused on strict positive/negative sentiment analysis, the three dominant theories (Marsella et al., 2010;Stelmack and Stalikas, 1991) of emotion agree that humans express or operate with much more nuanced emotion representations.

tweets or reviews, in recent times, include non-standard representations of emotion like emoticons, emojis etc.


(2004) describe an interesting approach for extracting collocations (i.e., word sketches), and to build a concordance and thesaurus.

they extract grammatical relations of a lexicon using hand-crafted rules, count the frequency of a word which is connected to another word by a specific grammatical relation (e.g., SUBJ OF, OBJ OF) (Lin, 1998), and weigh its collocations by logDice (Rychly, 2008).


Our system, although simple, achieves promising results for this task.

our system obtains an F 1 score of 95.47% on Vietnamese spoonerism dataset.


Basically, spoonerism occurs when switching parts of one syllable to the other syllable to form Vietnamese words with different meaning.

vietnamese syllable is a combination of consonants, vowels, and tone.


Thus, we use the development set to find the optimized thresholds.

we calculate a score for each Type 1 candidate in the development set and find the thresholds that maximize the F 1 score of detecting Type 1 spoonerism on this set.


Based on the task requirements, logical constraints of the attention mechanism have been designed to control the learning process of network, which enhances the controllability in learning.

a logical constraint is an operation that affects the attention weight.


Table 2 and Table 3 illustrate the experimental results.

experiments in Table 2 use depth module for feature extraction and in Table 3 use shallow module to extract feature.


In this paper, we proposed a method for combining multiple embeddings for the domain adaptation of sentiment analysis.

the document vector v was obtained from BOW model, embedding e was obtained by doc2vec, embedding g was obtained by SVD, and the vector [v : e : g] connecting the three vectors is used as the feature vector.


This study investigated the processing of possibility hedges.

it tested whether participants ignored possibility hedges when processing medical diagnoses and medical advice that contradicted their expectation.


the study investigated the processing of possibility hedges when there was a conflict between readers' expectation on the severity of their illnesses and the medical knowledge (i.e., medical diagnoses and medical advice) given by doctors.


There was one within-item factor, namely possibility.

for each item pair, the use of hedges suggesting possibility for the medical diagnosis and medical advice to be true were manipulated.


For the other four items, the majority of ratings was in between.

the perceived severity of the symptoms was not clear cut.


The use of high-possibility hedges thus, showed a strong sign that participants' perceived severity was wrong.

the doctors' saying strongly contradicted participants' expectation.


The medians of the degree of surprise ranging from 3 to 5 suggested that the experimental stimuli worked as planned.

the stimuli could make participants surprised when reading the continuation of the texts.


Another possible explanation for the dissimilar effect of possibility on the two types of items is that, the lack of possibility effect on the perceived-to-be-serious type resulted from the difference in the stimuli used.

the medical advice present in the items of perceived-to-beserious type might have enhanced credibility of the doctors' saying.


However, previous work has some limitations.

most studies relying on feature engineering conducted their experiments only on small datasets, making it difficult to achieve high classification performance.


In order to see the difference between the conventional network and the feature network built upon human intuition, we build a baseline model by replacing the feature network of FAN with RNN.

each post vector is generated by bidirectional RNN.


If the F1 weight of a post is low, most posts do not seem to be related to depressive mentions, because the keywords we selected for depressive symptoms rarely appear throughout the entire posts.

a post without features we established shows biased feature weights toward F1.


Certainly, there exists a large body of research on extractive summarization, utilizing many different approaches (see (Nenkova and MecKeown, 2012;Yogan et al., 2016) for details on extractive summarization techniques), but these works focus on summarization, and thus, evaluation is done out of the direct context of the document.

summaries are scored based on their completeness, coherence, and importance as a standalone paragraph rather than in line with the text.


POS tags are annotated for the same articles as the NER task.

we used the Mainichi Shimbun annotated with POS and NER tags.


The analysis in LL study relies on photography and visual analysis in which the core data gathering method is to engage in photography that thoroughly document defined spaces in the institutions' buildings (Akindelle, 2011, p.5).

the researcher went to different buildings where different colleges can be located.


This task aims to support human experts to detect suspicious news articles to be verified, which is costly but a crucial step before verifying the truthfulness of the articles.

in this task, given a set of posts on SNS referring to a news article, the goal is to judge whether the article is suspicious or not.


To mitigate the problem, we aim to automate suspicious news detection.

we develop computational models for detecting suspicious news articles to be verified by human experts.


We formalize suspicious news detection as a task.

in this task, given a set of posts on SNS that refer to a news article, the goal is to judge whether the article is suspicious or not.


Not only precision, recall and F1 scores, we evaluate the performance using a ranking criterion, Recall@K. In this work, since we assume that we send articles to human fact-checking experts in order of the suspiciousness scores, Recall@K is suitable for evaluating the ability of models to properly rank the suspicious articles.

recall@K evaluates the propotion of the correct suspicious articles in the top-K ranked ones, where T is the number of the total articles in the test set, and b i is a binary value, i.e., 1 if the i-th ranked article is suspicious and 0 otherwise.


Then we use the highest score among them as the score of y (a) .

we calculate the score of y (a) as follows, SCORE(y (a) ) = max x (a) ∈X (a) P θ (y = 1|x (a) ) .


This paper argues that external arguments are introduced as thematically underspecified and their thematic interpretation is determined at the C-I interface based on their context of occurrence.

this paper first argues for the underspecified nature of external arguments by showing the facts concerning the distribution of interpretationally variable arguments, and it then argues that the analysis invoking thematic underspecification fares better than those assuming different heads responsible for different interpretations.


The aim of this paper is to show that a head like have is more pervasive than previously thought.

i argue that a head introducing an external argument introduces a thematically underspecified argument, which receives interpretation depending on the context of occurrence (cf.


It is possible to disambiguate (3) into one of the two interpretations of the subject.

the sentence in (3) can be continued with either (4) or (5) below.


Note that, even when the subject is not a direct agent of the event, it assumes some control over the event as a whole, as can be evidenced by the soo suru ('do so') replacement test, which singles out volitional or willful subjects.


Moreover, the availability of the nonagentive interpretation depends on the nature of the event involved.

for a non-agentive argument to appear as the subject, the verb phrase must describe an event that is typically associated with a particular type of agent with expertise (e.g., surgeons, barbers, etc.).


Ethical considerations from the use of the participant's personal data were taken into ac-count.

an informed consent form was prepared to provide participants with details on the procedures for data collection, storage and disposal, and how confidentiality of data and anonymity of the participant will be preserved during the study.


In this paper, we studied Facebook's potential as a knowledge resource for story generation.

we look at a user's personal information, likes, events, and posts in providing the needed knowledge, specifically about story characters, their attributes, events (activities and actions), and the locations where these take place.


Using the must construction as an example, this paper attempts to demonstrate the usefulness of a constructionist approach in combination with the conversational analysis to provide a more precise and detailed description of modality.

we examine (1) the central members of must constructions in spoken corpus, and (2) the generalized functions of the central members of must constructions.


The finding is in line with Goldberg"s (2006) observation that the meanings of high frequency items that fill in the schematic slots tend to resemble the meanings expressed by the constructions where they occur.

the fuzzy meanings associated with the objects that appear in [there+must+be+some] are compatible with the topic extending function of the construction.


Examining [I+must+admit+I], we found that the construction is typically preceded by a positive or neutral comment on the state of affairs in the prior turn but is usually followed by a strong opinion or disagreement incorporating the speaker"s feelings (e.g., disappointed, fed up), conviction (e.g., sure, convinced), disposition (e.g., dowdy, older) or desire (e.g., looking forward to, keen) such as "I must admit I don't think we get offer really frequently", or "I must admit I'm getting a bit fed up with it now".

[I+must+admit+I] performs an orienting function by directing the communicative partner"s attention to an opposite view point or a negative remark that may sound disagreeable or upsetting.


The conversational analysis revealed that the construction played an important role in facilitating the transmission of information among multiple speakers.

it is used to develop subtopics which serve as links to the prior and subsequent utterances.


However, their measurements were focusing only on speed, i.e., the rate of articulation.

they only measured the transmission efficiency.


It takes a sentence represented as a sequence of word embeddings as input.

a sentence s with n words, and each word being represented by a word embedding of dimension d, will have a representation of size n × d. This is followed by a convolution layer.


However, this kind of dictionary has the same major problem as the lexicons it contains may not have fixed nature such as positive or negative in all sentences.

a lexicon may be positive in one sentence but negative in another.


In general, nouns or verbs (contextual words) are represented by an adjective (sentiment word).

in the aforementioned examples, the price and quality are extracted as contextual information for the sentiment word high .


In the reviews of tiki.vn, because of the large bias in the number of positive and negative reviews, the values of precision and recall showed a big difference.

no remarkable difference was identified because the created VietSentiLex lexicon was mainly used in describing hotel-related topics.


In addition, the number of contextual words extracted from the Binh and Son corpus was not sufficient to predict all circumstances.

contextual words in the VietSentiLex ambiguous lexicon did not exist for products and services other than hotels.


Other combinations can be treated as variations from "standard units".

a disyllabic structure works best for Chinese words and haplology should maintain disyllabicity when possible.


Wu (2012a) compares běnlái and yuánlái and argues that the essential difference between běnlái and yuánlái lies in the ways how contrast is satisfied: běnái allows for one-way contrast, as pointed out by Wu & Kuo (2012), but yuánlái two-way contrast.

for běnlái, a proposition is true at a time in the past of a reference time, but not true at the reference time, whereas, for yuánlái, a proposition can be true at a time in the past of a reference time, but not true at the reference time, or vice versa.


In order to emphasize that there is no change in his height and that he has been this tall all the time, (2a) is uttered.

there is still a contrast in the context for (2a).


As a result, yídìng in this example expresses strong epistemic necessity.

it is an epistemic necessary modal expression.


Bù yídìng 'not necessarily' partially negates the truth of a proposition, resulting in a p or not p reading.

for (6), bù yídìng huì 'not necessarily HUÌ' means that he will or will not be present.


Moreover, even though Portner (2009: 122-123) suggests that alethic modality is a type of epistemic modality, yet, mode of truth should not be confused with mode of knowing based on the above discussion on explicit syntactic realization of a constituent indicating dependence on the speaker's knowledge.

alethic modality does not have to be a type of epistemic modality.


Thirdly, yuánlái2 presents a new finding of a proposition, which is true at a reference time but is undetermined concerning the truth value at a time in the past of the reference time.

apparently, yuánlái2 involves mode of truth.


If the above argument of yuánlái2 as an evidential marker that expresses alethic modality is on the right track, then a very subtle, but interesting, im-plication arises.

it seems possible that (at least some kind of) evidentiality is not a subtype of epistemic modality.


Before summarizing this section, one question needs to be addressed.

if yuánlái2 is evidential and alethic, how about yuánlái1 (and běnlái since yuánlái1 and běnlái is mostly interchangeable)?


Thus, this paper aims to examine to what degree the core-peripheral distinction for split intransitivity is cross-linguistically consistent and if it is possible for L2 learners of English to acquire the distinction at the syntax-lexicon interface.

the present study examines whether native English speakers and Chinese-speaking L2 learners are sensitive to the lexical constraints underlying core-peripheral distinction by testing their judgments of core and peripheral verbs in two unaccusative diagnostics.


is a non-leaf-node index in path of Huffman code, is a Boolean answer of the binary decision.

we have matrix V and I, corresponding, respectively, to vectors of leaf-node and non-leaf-node.


When we deal with intraand inter-sentential anaphora in a single model, we need to cope with a large search space for the an-tecedent.

adopting a machine learning approach, we have a far larger number of negative instances than that of positive instances.


co-reference, we regarded all the phrases referring to the correct entity as the correct answer based on the co-reference information annotated in the corpus.

we consider our system outputs as correct antecedent if our system locates any of the antecedents which corefer to the correct one.


Sharing the signifiers clearly does not entail affinity for the linguistic status, but sharing the signified could mean sharing the (lexical) semantic system representation of conceptual systems.

shared orthography should not be discounted as evidence for a linguistic relationship a priori.


All the three variables were found to influence the rating scores of native speakers: χ 2 (1) = 347.83, p < .001 for 'relation'; χ 2 (1) = 173.31, p < .001 for 'combinability'; and χ 2 (1) = 4107.5, p < .001 for 'usage'.

characters with higher combinability radicals tended to be rated higher; characters whose meanings are still in use also received much higher scores.


This assumption finds supports in word production literature; spoken word recognition involves phonological activation (McQueen et al., 1995); visual word processing occurs along both visual and phonological pathways (Cotheart et al., 1993;Seidenberg and McClelland 1989) .

the phonological activation occurred in both spoken and visual word recognition.


They may not be proficient enough to identify the phonological cues in auditory experiments and use the cues to activate the corresponding letters of the word, whereas high proficiency participants may use the cues to respond more accurately.

high proficiency participants may have stronger phonological awareness and encoding ability.


Therefore, the answer to why the A-scrambled non-subject in SpTP controls only certain subject properties could be related to the distinction between high/Pivot and low/PA subject properties in Korean.

if reflexive binding and wide scope are properties available to a nominal in the high/derived subject position while HA and PC are properties of the low/thematic subject position, the fact that the A-scrambled object fails to control the latter two properties would not constitute a counterexample to the analysis that takes Ascrambling to involve movement to SpTP.


Controller of subject honorification (Honorific Agreement) low/PA subject properties while others are high/Pivot subject properties.

he argued that HA and PC are PA/lower, subject properties, which are not controlled by high/Pivot subject (cf.


And while the difference between the sentences with canonical order and those with scrambled order was significant statistically with subject controllers (V = 97324, p-value = 0.02168*), as shown from a Wilcoxon test, sentences with nonsubject controllers showed no difference between canonical and scrambled orders (V = 96376, pvalue = 0.3059, n.s.).

scrambling of non-subject NP that is [+pl] to a potential subject position cannot improve the acceptability of the sentences (white bar in Non-subject).


However, LDA 3 results in Table 5 suggest that the series of semitone normalizations perform better than the other normalization methods.

sT-AvgF0, sT-xH, sT-, and sT-xmax show better performances than the baseline in both regions.


These lies cannot replicate the true situation of lying (Fitzpatrick & Bachenko, 2012).

participants know that they will not be punished for lying in such studies.


However, the difference is not significant.

there is little difference between deceptive and truthful texts when participants depicted the basic information of the teachers they like or dislike.


The attention mechanism is similar to the one proposed by Yang et al.

(2016), which is defined as follows: to say, we first compute the u i as a hidden representation of the hidden states of LSTM h i for i th input token, i.e., Then, we measure the importance of the i th token with a trainable vector u w and get a normalized importance weight α i through a softmax function.


However, in multi-document summarization, presented content should be relevant to the query and novel with respect to what is already mentioned in the summary.

salience comprises both relevance and novelty.


The most interesting difference was the fact that verb forms in Spanish provide irrealis information.

the conditional tense and the imperative and subjunctive moods often serve to indicate that the situation being referred to is not in fact the case.


The general rule is that the type of a vowel, short or long, is preserved in all word forms.

contexts, a vowel is kept long by doubling it: (1) ma-ken maak (to make, INF1st PER.SG.PRES) After removing the -en sux from the innitive, we get the form *mak and the vowel turns into a short one.


This method increases the fullparse rate from the native supertagger (1-best heuristic) by a factor of 3 in the first category, by a factor of 11 in the second category and by the factor of 21 in the third category.

the effects of the trained probability model in long sentences are more than short sentences.


If many of the words occurring in the definition have a strong component in one of the dimensions, then this dimension will be strong in the second order vector.

the second order vector helps pinning down the strength of the different dimensions in a given definition.


As a result, similarity measures were applied between the definitions of verbs corresponding to the same type of usage.

the definitions of a synonym associated with a given verb usage (reflexive vs. non-reflexive) were compared only with the definitions of this particular usage.


First, they aim to extract synonyms from linguistic data and thereby often yield "associative" lexicons rather than synonymic ones.

these approaches yield lexicons which often associate with a word, synonyms but also antonyms, hypernyms or simply words that belong to the same semantic field.


First, we will explore ways of improving these results.

we will investigate in how much the structure of a dictionary entry can be used to enrich a definition.


We combine well-established features used for other languages with language-specific lexical, syntactic and morphological information.

we make use of the rich tagset annotation of the BulTreeBank (680 morpho-syntactic tags), from which we derive suitable task-specific tagsets (local and nonlocal).


Current search engines target broad coverage of information, at the cost of providing limited support for well defined verticals.

an increasingly large number of users, consisting primarily of students, instructors and selftaught learners, are often seeking educational materials online, to use as standalone instructional materials or to supplement existing class resources.


An iterative bootstrapping framework is presented, based on the idea that documents, even those with a low similarity score, containing one pair of parallel sentences must contain others.

the proposed method works well for corpora with very disparate contents.


In this paper, we investigate the problem of multi-document summarization, where a summary is created from a set of related documents and optionally fulfills a specific information need of a user.

we focus on generating an extractive summary by selecting sentences from a document cluster [8].


Predictions concerning "echo" involve that the episodic memories will be more sensitive to probe episodes that are well represented in the memory and less sensible to probe episodes that are less represented.

target vectors will be more sensible to typical documents and less sensible to non-typical documents.


In the sentence the subject zentral nuklearrak (nuclear power station), in absolutive case, and the auxiliary verb, dute, which needs the subject to be in ergative case, do not agree.

the first rule asks that the current word (which should be the main verb) has a subject, and this subject has a modifier (which contains the grammatical case).


Several recent studies have stressed the benefits of going beyond traditional single-document extraction and taking advantage of information redundancy.

[3,16,21,22,23,24,25] have emphasized this potential in their work.


Another problem is 'out of the blue' non-neutral polarity.

combinations of two or more neutral words might yield a non-neutral polarity.


As mentioned above, we exploit corpus annotations and use linguistic knowledge in order to obtain subcategorisation information for adjectives.

we refer to constituent types (to identify APs and their components) as well as to functions associated with direct dependents of verbs, especially with respect to predicative adjectives.


Therefore, we use linguistic knowledge, applied to corpus annotations, in order to identify predicateargument structure of adjectives.

we aim at providing a 'normalized' valence, i.e., to separate constituents which occur with individual adjectives from components of productive constructions (elements which can appear with almost any adjective).


Therefore, no other linguistic observations can help us specify the status of PPs in APs.

there is no general rule which would permit to distinguish a PP complement of an adjective from a PP attached to an adjective in complex NP restructured constructions, [11].


The method uses a knowledge-based Word Sense Disambiguation algorithm for linking FrameNet lexical units to WordNet synsets.

we exploit a graph-based Word Sense Disambiguation algorithm that uses a large-scale knowledge-base derived from WordNet.


Thus, SSI-Dijkstra provides no results when there are no monosemous terms in W. In this case, before applying SSI, the set of the LUs corresponding to a frame (the words included in W) have been ordered by polysemy degree.

the less polysemous words in W are processed first.


Thus, this algorithm expects that the first sense in WN will be correct for most of the words in W. Regarding ASI, this algorithm expects that the words in W (corresponding to a very close semantic field) will establish many close path connections between different synsets of the same word (because of the fine-grained sense distinction of WN).

at each step, both the original SSI and also the SSI-Dijkstra algorithms only consider the set I of already interpreted words to disambiguate the next word of P. the remaining words of P are not used in the disambiguation process.


SSI-Dijkstra-FirstSenses-P (hereinafter FSP) and SSI-Dijkstra-AllSenses-P (hereinafter ASP).

when a word is being disambiguated, these two versions consider the set I of already interpreted words of w and also the rest of words remaining in P. at each step, the algorithm selects the word sense which is closer to the set I of already disambiguated words and the remaining words of P all together.


This data set also has 73 frames having only polysemous LUs (20% of the total).

these frames do not have monosemous LUs.


Because they occur only once, they only have one possible meaning within this corpus.

high frequency words can be naturally disambiguated -temporarily -by the simple means of removing data.


In addition, since we can safely assume that "perfect alignments" yield good translations, the remaining parts of the sentences they appear on are likely to be translations of each other as well [3].

each "perfect alignment" yields up to two multilingual alignments per line: 1. the sequence of words that consists of the "perfect alignment" itself, preserving word order from the sentence; 2. the complementary of this sequence on the line (i.e., its context), ordered as well.


We use the principle proposed in [9] to compute phrase translation probabilities, except that we generalize it to multilingual contexts: since there is no "source" and "target" languages in our multilingual alignments, each language becomes the "source" in turn, and all remaining languages together become a single "target" one.

assuming an input corpus in L languages, a score is computed for each language i (1 ≤ i ≤ L).


Currently, the major problem of TimeML lies on the lack of resources, specially the lack of corpora for languages other than English.

this work is focused on the benefits that available semantic networks and semantic roles corpora can introduce to TE identification.


WordNet [3] and Eu-roWordNet [23] represent the most used semantic networks for English and European languages respectively.

in temporal expression identification task, semantic networks have been used in the following works: Negri et al.


• Spurious (8% EN / 6% ES): False positives have been reduced drastically by the application of the method based on semantic networks defined in this paper, which confirms that the proposed hypothesis is valid for this task.

it decreases TIPSem spurious errors from 27% to 8% for English and from 25% to 6% for Spanish.


Even though redundancy does increase reading times, matching i and j may still require relatively little cognitive effort when both i and j share a large number of properties (or words) if compared to matching a more dissimilar description pair.

we hypothesize that for a closely-related pair of descriptions (i.e., an anaphor and a c1 object, or an anaphor and the actual antecedent term) the readers may benefit from some form of shallow processing to quickly decide, for example, that the reference "the small black cat" is not the same as (c1) "the small white cat", but that it does co-refer with an antecedent term "the black cat".


Every normalisation activates a set of concepts in an ontology embedded in our model of Conceptual Structure.

every word activates those concepts that are lexicalised by the word's normalisation.


The use of this metadata is predicated on the assumption that it is semantically related to the document under consideration.

the document title should reflect the document content, and likewise the text of links should accurately describe the target document-practices which are mandated by authoritative hypertext style guides [24,10,9,4].


We therefore propose that wikis may benefit from special-purpose search engines optimized for their particular features.

we believe that the semantic coherence of documents enforced by link text-document title correspondence makes wikis an excellent candidate for processing with latent semantic analysis [13,20], a factor analysis technique which is able to discover latent semantic relations between terms and documents.


However, simple cumulative sums do not suffice.

individual (sub)contexts' scores need to be weighted because not all of them are equally salient: atomic (sub)contexts are evidently not very important, for example.


This should also change in the next version of the Leffe.

9 additional syntactic features such as control information are not shown.


Structure-meaning duality is actually at the heart of language and is perhaps more pervasive than has been realized.

it can be emphasized that this duality does not in itself diminish the complexity of language as we know it, rather it adds to it.


Interestingly, being an emergent level of fundamentality, this duality can never be broken down into either meaning or structure separately.

if one tries to treat meaning representations or specifications in a way thinking that they are actually representing meaning which, to them, is a phenomenologically different entity altogether even if it stands for the structure in question, they are misled into a wrong direction.


The comparison of the final results of the joint and the isolated system presented in Table 12 Table 11: Labeled attachment score (LAS) for syntactic dependencies and F scores of semantic dependencies per language in the joint system for syntactic dependencies, and a drop in performance of the isolated system for semantic dependencies.

the isolated system produces considerably lower recall rates.


By combining each of the label elements with the vectors that represent the instances, this Φ function generates a range of features in a vector corresponding to each label element.

the weights of each feature can be determined for each label element.


In this paper, we train and classify shifting the segments by 1 utterance.

the second utterance from an example is the first utterance on the next example.


The second method extracts co-occurrence statistics for each of the two words of interest from a respective monolingual corpus using a dependency parser.

verbs are used as distributional features when comparing nouns.


Given two sets of relational patterns P 1 and P 2 for the relations R1 and R2, a pattern p k belonging to P 1 is a MRP for R1 if condition (1) holds.

a pattern p is minimal for a relation R if none of the patterns for the other relations can be derived from p (i.e.


In this paper, we also focus on the IMT approach to CAT.

we propose a new IMT engine based on the generation of partial alignments at phrase-level.


The key difference between their system and the system we propose is that they use error-correcting techniques instead of smoothing techniques to assign probabilities to unseen events.

the error correcting costs are introduced as an additional weight in their log-linear model.


In this way, the search process only requires the use of a regular SMT system which filters its phrase-table in order to obtain those translations of f that are compatible with e. As noted in [16], this technique has no practical interest when applied on regular tasks.

the technique is not applicable when the alignments cannot be generated due to coverage problems of the phrase-based model (i.e.


We have chosen three different techniques for combining estimators: Linear interpolation, Backing-off, and Log-linear interpolation.

we have implemented combinations of two estimators, a phrase-based model estimator (ML, GT, AD, KN or SD estimator) and the LEX estimator.


The concept of partial phrase-alignment is similar to the concept of complete phrase alignment described in section 4.

we define a partial alignment between f and e p as the set S ′ of ordered pairs that contains all the words of e p and only a subset of the words of f .


Such pruning techniques include hypotheses recombination, stack length limitation and restrictions on the maximum number of target phrases that can be linked to an unaligned source phrase during the expansion process.

in those cases where e p has not already been generated, only a subset of the strings contained in the set S er are considered as candidates for the expansion process.


As can be seen, our system obtains similar results and in some cases clearly outperforms the results obtained by these IMT systems.

our results were better than those obtained by the SFST and the AT systems.


Moreover, we show various insightful statistics and correlations within and across three research fields: Linguistics, Computational Linguistics, and Education.

we show how topics change over time within each field, what relations and influences exist between topics within and across fields, as well as what trends can be established for some of the world's natural languages.


Based on various topic models which classify research papers into topic and language categories, the system displays a series of statistics, correlations, and graphics which show the dynamics of topics and local and global trends based on the proceedings of the major conferences and journals in the three fields over many years.

we show how topics change over time within each field, what relations exist between topics, what temporal correlations and topic influences can be determined across fields, as well as what trends can be established for some of the world's natural languages.


Another approach to the analysis of scientific research relies on topic models which uncover structures used to explore text collections.

they divide documents according to their topics and use the hidden structure to determine similarity between documents.


We categorize both by topics   Some, albeit only a small fraction, of the Linguistics papers were already categorized (with overlap) in their original publications.

language, Journal of linguistics, and linguistic Inquiry provided 320 labels for 190 of these papers.


Moreover, we gave various insightful statistics and correlations within and across three research fields: Linguistics, Computational Linguistics, and Education.

we showed a number of trends in each field along with relations between topics, temporal correlations and topic influences across fields, as well as language trends.


Nodes PP4 and PP5 represent the extremities of the chain and the underspecified dominance relation between them means that the number of elements of the chain is undetermined.

it can be null when PP5 is identified with PP4.


We describe a novel language-independent approach to the task of determining the polarity, positive or negative, of the author's opinion on a specific topic in natural language text.

weights are assigned to attributes, individual words or word bi-grams, based on their position and on their likelihood of being subjective.


Sentiment analysis aims to determine the exact polarity of a subjective expression.

in [3] there is an effort using semi-supervised machine learning methods to determine orientation of subjective terms by exploiting information given in glosses provided by WordNet.


Specifically in [3] there is an effort using semi-supervised machine learning methods to determine orientation of subjective terms by exploiting information given in glosses provided by WordNet.

this approach is based on the assumption that terms with similar orientation tend to have similar glosses.


To represent a text set using n-gram graphs, we have implemented an update/merge operator between n-gram graphs of the same rank.

given two graphs, G 1 and G 2 , each representing a subset of the set of texts, we create a single graph that represents the merging of the two text subsets: The weights of the resulting graph's edges are calculated as follows: W i (e) = W 1 (e)+(W 2 (e)−W 1 (e))×l.


The results for the four polarity alignment tasks concern disagreement upon polarity alignment between annotators.

for metaphors, annotators disagreed in 10 senses for manual and 13 senses for automatic disambiguation, out of a total of 128 senses.


In the next section we present previous work, followed by an analysis of semantic relations.

we provide a classification of clusters of near-miss and overlapping relations based on empirical observations.


Better results are obtained by SNoW and the SNoW and Semeval systems due to their contextual features.

these systems classified well the near miss examples in Table 6 due to various lexical and syntactic features such as verbs and the prepositions "from" and "for".


We also performed a quick error analysis.

we looked at some of the examples which were misclassified by the Semeval system.


We use the same evaluation metrics as used for the lexical substitution task at Semeval-2007.

we measure the precision and the recall for four subtasks: best normal, which measures the precision and recall obtained when the first synonym provided by the system is selected; best mode, which is similar to best normal, but it gives credit only if the first synonym returned by the system matches the synonym in the gold standard data set that was most frequently selected by the annotators; out of ten (oot) normal, which is similar to best normal, but it measures the precision and recall for the top ten synonyms suggested by the system; and out of ten (oot) mode, which is similar to best mode, but it again considers the top ten synonyms returned by the system rather than just one.


Size-bounded partitioning, on the other hand, can be used to obtain pre-clusters of manageable sizes, while at the same time committing much less understemming errors.

partition M (500) seems like a reasonable trade-off between  After partitioning the corpus into pre-clusters of a manageable size, we applied hierarchical agglomerative clustering on each pre-cluster separately.


Thus, we use the word spelling, a word form, as a substitution for its sound.

we use the letter form of transliterated Russian words and Romanian words for machine learning classification of words' affects.


The results reflect different features of the two algorithms that had not been highlighted in the previous evaluations.

algorithm 2 achieves a better performance on precision for evaluation Type 1, but the overall recall value are worse for both types.


In the future, we plan to improve the projection algorithm exploiting all annotation layers present in the FrameNet corpus.

information about the grammatical function of frame elements could help improving constituent alignment and candidates selection.


The search engine which has been developed in the LT4eL project is based on the modules previously described.

when a user types a query, the search words are looked up in the lexicons of the chosen language.


Several suggestions have been made in the literature in this respect.

nLP techniques can be adopted to extract terms/concepts, definitions and relations from learning material.


The integrated approach has been tested to extend the LT4eL lexicalized domain ontology to the music domain.

on the basis of the NLP techniques proposed by [2], we have developed a kick-off ontology consisting of 46 classes related to each other by the is-a relation.


Part of the enduring interest is that name tagging continues to provide technical challenges that help drive research.

while the fundamentals of training a name tagger are well understood, such barriers to practical application as robust coverage and transfer training remain active research areas.


Some subtleties preclude this method from being entirely automatic.

company names regularly include prepositions, conjunctions, or punctuation, as in: • "Helen of Troy Ltd." • "JP Morgan Chase and Co." • "Wong's Kong King (Holdings) Ltd." The instance finder must capture these non-noun atoms in such cases, but must also exclude them in others, as in: • "Jeff Schuman of Keefer Bruyette Woods."


As mentioned above, inverted index extraction (IIE) is one of the earliest attempts at using dictionary definitions to extract synonyms or related words.

for a given target word t , the entire dictionary is scanned in search of words whose definition texts contain t, and such words are considered related to t. Since both synonymy and relatedness are symmetric, it is equivalent to say that every definiendum is related to all its definientia, or that the dictionary graph is an unweighted, undirected graph where every pair of neighbors is considered equally related.


Note that, in order to make fair comparisons with IIE and PbE, it is necessary to incorporate the dictionary graph into MaxEnt method.

given a target word t, after extracting synonyms from its own definientia, we again go through other words' definitions in the dictionary; if t appears in the definition of another word w and is classified as a synonym, then w is taken as a synonym of t. So far in our discussion, the dictionary graph has been assumed to be undirected.


However, manual evaluation of extracted keyphrases is very costly and timeconsuming.

it is not suited for any kind of parameter tuning, as the output of each new system configuration involves manual re-evaluation.


The feature set in the S10 system is unable to handle these new challenges.

s10 tends to choose overly  general hypernyms far too often.


For an extracted predicate p for e, we assign arguments to p based on the semantic role label or labels with which it is most commonly associated in the annotated corpus.

for each possible semantic role r, we count how often p occurs in a phrase that is an argument to e and is annotated with role r. We also count how often p occurs as part of any phrase that is annotated with role r. Let score(e, r, p) denote the sum of these two counts.


Unfortunately, one is often constrained by the lack of resources, tools or language experts, for instance when dealing with resource-scarce languages.

the elaboration of a POS tagger is a delicate issue.


We have previously shown (Loftsson et al., 2009) that a significant improvement in tagging accuracy is obtainable by running a serial combination of IceTagger and a HMM tagger (TriTagger).

the best result was obtained by making the HMM perform initial disambiguation only with regard to the word class (the first letter of a tag), then running IceTagger, and finally by making the HMM disambiguate words that IceTagger was not able to fully disambiguate.


We then create a new dictionary D N EW (based on D BIN ) in which frequency information for some of its tag profiles comes from D M IM .

we use the following procedure:  Table 4: Average tagging accuracy (%) using dictionaries extended with most of the data from BÍN and with arranged tag profiles for some of the words.


However, TS is an especially challenging Natural Language Processing (NLP) task, since the generation of summaries depends on a wide range of issues, such as the summarization input, output or purpose.

the type of text or domain we deal with is of great importance in TS, since each domain has its particular features, and they need to be treated accordingly.


Moreover, new scenarios, such as the generation of summaries that can be used as image captions (Aker and Gaizauskas, 2009;Plaza et al., 2010;Aker and Gaizauskas, 2010a), have recently drawn special attention in recent years.

this image caption generation task has been automatically approached by analyzing image-related text from the immediate context of the image, for instance, the surrounding text in HTML documents (Mori et al., 2000;Deschacht and Moens, 2007).


A hypergraph (H) is a generic graph wherein edges can connect any number of vertices and are called hyperedges.


The inclusion of shallow-transfer rules provides a statistically-significant improvement over the dictionaries for all the training corpus sizes in the Spanish-English out-of-domain evaluation scenario and for the smallest ones in the in-domain tests.

shallow-transfer rules are effective when the decoder chooses a high proportion of Apertium-generated phrase pairs.


The small standard deviation supports the assumption that PE is 40% faster than HT, regardless of the translator and the source of automatic translations.

translating from scratch consistently takes 70% longer (HT/PE) than post-editing the same sentence.


3 By atomic we mean that the symbols are not analyzedin terms of their phonetic features-and treated by the baseline algorithm as atoms.

the model has no notion of identity of symbols across the languages!


The parameters L(e), or P (e), for every observed event e, are computed from the change in the total code-length-the change that corresponds to the cost of adjoining the new event e to the set of previously observed events E: Combining eqs.

1 and 3 gives the probability: P (e) = c(e) + 1 e c(e ) + |E| the cost of the most probable complete alignment of the two words will be stored in the bottom-right cell, V (σ n , τ m ), marked .


Thus, hyper-jump from cell marked X means that we code the remaining symbols [σ i+1 ...σ n ] in σ and [τ j+1 ...τ m ] in τ separately, not using the global count matrix.

to align σ and τ , we first code the symbols up to X jointly, prequentially, using the global count matrix.


On the contrary, the SMT system provides translations almost every time, but they may be incorrect.

information coming from the external resource and the SMT system can complement each other, the former boosting precision and the latter ensuring recall.


As shown in Table 1, the ratios of the pauses to the stops are 2.40 in Sinica dataset and 2.25 in Master dataset.

the classification between the class "start" and the class "non-start" is unbalanced.


We deal with the intrasentence connective in the similar way.

the corresponding intra-forward and intrabackward features will be increased accordingly.


Based on the results obtained in the second batch of experiments, we investigated whether some particular genres actually decrease performance when training on all data.

does excluding outlier genres from training data increase performance?


However, due to the vast amount of available information, users cannot cope with it, and therefore research into new methods and approaches based on Natural Language Processing (NLP) is crucial, thus resulting in considerable benefits for the society.

one of these NLP research areas is Text Summarisation (TS) which is essential to condense information keeping, at the same time, the most relevant facts or pieces of information.


We developed an extractive TS approach for each case.

we analysed: i) language-independent techniques (Subsection 3.1); ii) language-specific resources (Subsection 3.2); and iii) machine translation resources applied to a mono-lingual summariser (Subsection 3.3).


In light of this, we decided to develop a similar approach, but relying on named entities and concepts.

we focus on four languages (English, French, German and Spanish).


The idea behind this approach is to use an existing mono-lingual summariser for a specific lan-guage and then employ a machine translation system for obtaining the summaries in the different languages.

we employ the TS approach proposed in (Lloret and Palomar, 2009) that generates extractive summaries for English.


Multi-lingual Summarisers With the purpose of widening the analysis and verifying our results, we compared our LS approach to several current multi-lingual TS systems, that also produce extractive summaries as a result.

we selected: • Open Text Summarizer 11 (OTS).


This paper presented a comparative analysis of three widespread multi-lingual summarisation approaches in order to determine which one would be more suitable to adopt when tackling this task.

we studied: i) a languageindependent approach using the term frequency technique; ii) a language-specific approach, relying on specific linguistic resources for each of the target language (named entities recognisers and semantic resources); and finally, iii) a monolingual text summariser for English, whose output was then inputted to a machine translation system in order to generate summaries in the remaining languages.


In contrast, our work concerns the consolidation of fine-grained information about opinions to create non-extract-based opinion summaries, a rich, concise and useful representation of the opinions expressed in a document.

the opinion summaries produced by our system combine opinions from the same source and/or about the same topic and aggregate multiple opinions from the same source on the same topic in a meaningful way.


We compare the performance of our system to a baseline that creates one summary opinion for each fine-grained opinion.

each source and topic mention is considered unique and each opinion is in its own cluster.


An evaluation on a test corpus consisting of 618 event descriptions which refer to 523 real-world events revealed that the description of circa 10% of the events extracted by the mono-lingual systems could be refined.

an overall gain of 6,4% and 4,8% in recall and precision against the best monolingual system could be obtained respectively.


Next, each cluster is processed by NEXUS, the core event extraction engine, which initially performs shallow linguistic analysis, including, i.a., fine-grained tokenization, sentence splitting, domain-specific dictionary look-up (e.g., for the detection of numerical expressions, quantifiers, person titles, and for the labeling of key terms indicating unnamed person groups), and morphological analysis.

for morphological analysis an extended version of the full-form MUL-TEXT 3 lexica are used.


5 Secondly, analysing the entire text might involve handling complex language phenomena (e.g., anaphora resolution), which is hard and requires knowledge intensive processing.

in the context of developing an event extraction system capable of processing news in several languages tackling more complex language phenomena would involve a substantial effort to provide the necessary language-specific resources.


6 It is important to note that NEXUS detects only the main event for each news article cluster ('one sense per discourse') (Gale et al., 1992), and 6 language-specific instances of the system have been developed to cover news in English, Italian, Spanish, French, Portuguese, and Russian.

for each language extraction grammars and specialized lexica were acquired using weakly supervised ML techniques and validated by human experts.


For exploring the potential of cross-lingual information fusion a corpus consisting of crisis-related event descriptions automatically extracted by NEXUS on 22 randomly selected (non-continuous) days in 2010 from news in 6 languages has been prepared.

we focused on violent events and natural and manmade disasters.


In total, there were 4252 news articles that refer to the 51 events.

the average number of news articles per cluster which correspond to an event in the set of the 51 events is: 29 (all languages), 55 (English), 16 (Spanish), 21 (Italian), 29 (French), 21 (Portuguese), and 8 (Russian).


Although the experiments reported in this paper are preliminary, we strongly believe that the We are greatly indebted to all our colleagues in the OPTIMA action at the Joint Research Centre, who are working on EMM.

we would like to thank Hristo Tanev and Vanni Zavarella, who are working on the core of NEXUS and languagespecific versions of the system, and without whom the presented work would not have been possible.


Like many statistical NLP tasks in which smoothing is required to alleviate the problem of data sparseness, smoothing is vital here, too.

in the first few iterations, much more smoothing is required than in later iterations.


Adding this new information we are able to improve the previous results obtained by the original RST and we also improve the MFS results in Semeval-2 competition.

we provide a sort of supervised aid (i.e.


As can be seen, although there are a number of corpora and databases annotated for MWEs, they typically focus on only one specific type of MWE.

there are hardly any corpora that contain manual annotation for several types of English MWEs at the same time.


If a candidate synset represents a correct sense of Persian word, we expect the occurrence of this sense in a Persian corpus which contains that word.

for each Persian word w and each PWN synset t, ߠ ௪,௧ is considered as probability of selecting PWN synset t for Persian word w. : In order to estimate these parameters we can divide the number of times that a Persian word w occurs with PWN synset t in a Persian tagged corpus to the number of times that a Persian word w appears in that Persian tagged corpus.


Automatic authorship attribution, by its nature, is much more advantageous if it is domain (i.e., topic and/or genre) independent.

many real world problems that require authorship attribution may not have in-domain training data readily available.


We conjecture that mood words 2 will reveal unique emotional traits of each author.

either the use of certain types of mood words, or the lack of it, will reveal common mood or tone in documents that is orthogonal to the topics or genre.


First, we tested with increased number of n-grams.

instead of using top 100 3-grams per document, we experiment with top 500 3-grams per document.


In this experiment, we focus on 6 authors out of 14 authors considered in the previous dataset in order to make it easier to split training and test data based on disjoint topics.

we place books in fiction, essays and history categories in the training set, and the rest in the test set.


Also, one of the cor-relations we noticed was that the more endogamy in terms of inlinks, the less interwiki links it had.

the less permeated is a culture by other topics and then diverse, the less connections from abroad.


Note that for two of the relations, we achieve results that are better than the best results achieved at SemEval-1 Task 4, even by systems that used WordNet and the original search engine query.

for ORIGIN-ENTITY, we achieve up to 77.8% accuracy, which is statistically significantly better than the 72.8% at SemEval-1 Task 4.


One approach we pursue for identifying a suitable mention/chain relies on the semantic types that FrameNet specifies for frame elements.

we look up in FrameNet the semantic type(s) of the FE that is unexpressed.


6 If multiple chains remain that are compatible with the FE in question, we select between them by some criterion.

we prefer to link the FE to that chain that has the mention closest to the FE in question in terms of intervening leaf nodes.


The reason for this may be that we consider more varied antecedents.

we also consider full sentence antecedents.


With respect to related work, we do not include a start state and instead assume that the edit operation process starts in any of the edit states where the three starting parameters are defined to be the same as the transition parameters from the M state to one of the edit states including M. We base our application of the Pair HMMs on a number of requirements that were proposed by  in adapting Mackay and Kondrak's word similarity Pair HMM for computing transliteration similarity.

we ensure the use of distinct emission parameters in the deletion (X) and insertion (Y) states because of different writing systems.


We used similar parameters for transitions to and from the deletion and insertion states as in Mackay and Kondrak (2005).

we used the same parameter for staying in the X or Y state, another same parameter for moving from either X or Y to the end state, and the same parameter from the substitution state to the X or Y state.


We propose using similar information to predict the plausibility of arbitrary verb-noun pairs.

we aim to learn the distinguishing visual features of all nouns that are plausible arguments for a given verb.


We therefore first provide results on simulated OOV arguments (Section 4.1), where we assume no corpus-based knowledge is available to the DSP classifier.

we initially exclude corpus-based features from our models.


We use Keller and Lapata (2003)'s approach to obtain web-scale co-occurrence frequencies for the verb-noun pair.

we retrieve counts for the pattern "V Det N" from a web-scale Google N-gram corpus (Lin et al., 2010).


We limit ourselves to the use of simple bag-of-words models on which fuzzy and K-means clustering are applied.

the aim is twofold: firstly, the construction of a program which takes a segmented text as input and produces a categorization of sequences of clauses by clustering, based principally on POS-tags; secondly, the comparison of this clustering with the typology proposed by a human expert, corresponding to discourse types.


Second, some works have been emerging, which focus on polarity detection of texts based on contextual information such as the author, the reader or the text.

(Balahur and Steinberger, 2009) identified the main tasks for news opinion mining: (1) the definition of the target, (2) the separation of the good and bad news content from the good and bad sentiment expressed on the target and (3) the analysis of clearly marked opinion that is expressed explicitly, not needing interpretation or the use of world knowledge.


In particular, (Balahur and Steinberger, 2009) identified the main tasks for news opinion mining: (1) the definition of the target, (2) the separation of the good and bad news content from the good and bad sentiment expressed on the target and (3) the analysis of clearly marked opinion that is expressed explicitly, not needing interpretation or the use of world knowledge.

they show that it is important to distinguish three different possible views on newspaper articles: author, reader and text.


We conducted three different experiments with different feature sets: (1) unigrams, (2) unigrams plus the job role (JR) and (3) unigrams plus the "opinion maker role" (OMR).

the job role was extracted from the Open Calais metadata and the annotator added the "opinion maker role".


The majority of the adjectives are positive and there are few negative adjectives.

the first negative adjective is ranked 87.


Conversely, the negative language was not exaggerated, however the positive language was domain specific as for example, "win-win", "mission-critical" and exaggerated, as for example, "superb", "immense".

negative adjectives tended to have the lowest PMI scores.


On one hand, the quotations with a high level of rhetorical features are assumed to carry no useful information with respect to the financial market.

the quote makers who use rhetorical language are assumed to have the inferred role of "biased" and the groups of individuals who do not use rhetorical language are assumed to be "unbiased".


the data was split into two categories, "bluster", (i.e.


So, by applying this two-step strategy, we obtain improved results over the baseline presented in Table 1.

we performed a 10-fold cross validation with the unigrams of the quotations from each category individually as the only features.


The only slightly odd thing about the trie we use is that it contains arcs with unknown items, to mark the fact that roots have holes in them which can be filled in a variety of ways.

a single hole may be filled by either an (unwritten) short vowel or a (written) long vowel, depending on fine-grained syntactic factors.


This has a number of interpretations, as the active and passive forms of verbs meaning 'study' and 'teach', but even if we consider just one of these, say the active form of the version meaning 'study', we see that there are a number of possibilities.

it could occur in a context requiring an indicative form, e.g.


Two annotators then labeled the 10 aligned scenarios, recording which noun-phrases referred to the same participant.

the labelers were shown, in order, the sets of aligned event descriptions.


We constrain by definition the lexical entries with clause-bounded scope to take scope only at clauses.

for instance, here there are the lexical entries for not, no and every: After the full interpretation of the minimal clause which they appear in, the category C has to be converted to category S. one can use the following silent lexical entry: This step ensures that clauses (of category C) can be further processed as pieces of discourse (of category S), because all discourse connectors (such as the dot or if) are allowed to take only expressions of category S as arguments.


To the best of our knowledge this is the first data-driven approach which is developed for this language and differs from state-of-the-art approches developed for English for the use of specific lexical semantic features.

the Italian-specific semantic features have been obtained from a semi-automatically built event lexicon which has been derived from the SIM-PLE/CLIPS lexicon (Ruimy et al., 2003), following the proposal in Caselli (2009).


The TULE Converter implements two different sets of rules: a group for event recognition which takes into account morphological features (PoS) and dependency relations with a set of "event trigger expressions" and a group for event classification.

for classification, the TULE converter exploits the derived event lexicon for having access to the TimeML class(es) associated with each event lemma and then integrates this information with syntactic information.


The other two best performing models differ from the basic one for the combination of morphological features and presence of semantic features.

tIPSemIt FPC5 has 2 the reported results differ from those published in the referred paper as the tempEval test set was not used for the evaluation.


The TULE TimeML Converter does not suffer from this kind of issues, since the tagging approach adopted is different.

we have observed that all events realized by verbs were correctly annotated.


The performance improvement of the TIPSemIT FPC5Sem with respect to the other models is due to the contribution of the event lexicon.

we register an improvement in the classification of less frequent classes in the data such as I STATE (52% vs. 73%), ASPECTUAL (41% vs. 65%) and RE-PORTING (53% vs. 68%), with the exception of I ACTION.


However, the event lexicon is not perfect.

we have observed that the coverage of the lexicon, i.e.


Because the global maximum probability is determined by each local maximal probability, for a path of N symbols, we only need to store the information of the current node.

we need only store M probability values in order to find the maximal probability value.


The probability distribution on a factor graph with templates Θ becomes The task of finding meaningful skip chains corresponds to finding a set of templatesΘ describing A linear chain template θ lc with features g lc ( x, j) for all possible combinations of label variables is assumed to be present in all configurations.

in the following, the set of templates to select from is defined by properties a skip chain factor is added to connect two labeling variables of two tokens if a specified property holds (where we take every occurring feature specified for the linear chain into account, like bag-of-words, prefixes, suffixes of tokens as well as several regular expressions, the full set is given by ).


In this work we will adopt the following normal form for LTGs.

definition 4 An LTG in normal form is an LTG where the rules are constrained to have one of the forms: : only rules where at least one terminal symbol is produced together with a nonterminal symbol, and rules where the empty bistring is produced, are allowed.


It seems natural to consider the text of an article as related or context words, instead we use the text in the paragraphs containing links to an article as contexts.

instead of using the definition and description as context (Lesk, 1986;Pedersen, 2002), the words around realizations of a sense are used as context.


Our context vectors are generated from all unigrams (though larger n-grams can be considered) in all paragraphs containing links to a possible sense.

for each possible sense we use the database table of all the links to find all the pages referring to a particular sense.


We followed (Wiebe et al., 1999) in operationalizing the SUBJ vs. OBJ categories.

if the primary goal of a paragraph is perceived to be the objective reporting of information, it was labeled OBJ.


A further proof of this impossibility to differentiate semantically between the two PWN 2.0 synsets is the fact that in PWN3.0 the two different synsets have been merged into one: the latter.

(the former has been eliminated from the wordnet.)


For the experiment with lexical generalisation, functional words (stop words) were removed and all remaining (content) words were generalised using their WordNet synsets, that is, groups of synonym words.

we expanded the source and suspicious documents by replacing each of its content word by the words in all of its synsets from WordNet.


For the version of the corpus expanded with WordNet synsets, the matching is performed based on unigrams of synsets.

the number of common synsets of the source and suspicious documents is computed and then normalised by the total number of synsets in both suspicious and source documents, using the Jaccard coefficient: (2) In plagiarism detection tasks, it is essential to perform initial filtering with superficial techniques to reduce the number of potential source documents, and therefore the number of document pairs to be processed in the next stage.


Although the baseline is effective in detecting direct verbatim copies, the WordNet synsets matching is capable of achieving better results regardless of how the plagiarised texts have been produced.

this strategy has identified significantly more simulated and obfuscated plagiarism cases than the baseline.


Then, a Gaussian Mixture Model (GMM) predicts whether d is relevant, given a model of a priori relevant documents (which are the documents included in the development set, DEV) and the LM.

d is considered as relevant only if d is close enough both to the documents used to build the LM and to the DEV documents.


Let us notice that with λ between 0.30 and 0.90, the recall reaches 100% with a threshold of 10%.

in a more general way, our method gathers all the relevant NPs to serve as titles, at the top of its classification.


[1] accomplished a study for sentiment classification on English and Arabic inappropriate content.

they applied their methodologies on a U.S. supremacist forum for English and a Middle Eastern extremist group for Arabic language.


In order to compare the experiment for Arabic and English, we have translated OCA into English using an automatic Machine Translation (MT) tool freely available.

we have used the online translator provided by PROMT 5 .


The proficiency level measure is based on the progression in the learner answers.

when a SLL of Arabic writes a wrong verb, it distinguishes between this set of lexical error types: lexical category selection, pattern selection, tense selection, mood selection, subjectverb agreement, verb conjugation, connected pronouns and/or consonant, and vowel letters.


Fisher's exact test for (4) showed that observation of (4) didn't have enough statistical significance because p-value was 0.146 (>0.01).

the results indicated partial validity of our assumption about evidence being the first relation followed by agreement, which allowed us to say that evidenced structures tend to lead to success in discussions.


The results also show that, in general, using the reduced tagset does not significantly change the difficulty of the task.

giving up morphological information in the tagset in this specific case does not lead to higher tagging accuracy.


In order to find the sequence of edit operations that transforms one tree into another, such as the pair shown in Figure 1, the computation proceeds as follows: create a new matrix called δ 2 , which has the same dimensions as the matrix δ which is used to store the forest costs during ZS-TED to store the sequence of edit operations as a list.

when the values of δ are computed, the values of δ 2 are computed, by using the edit operation labels: "i" for an insertion, "d" for deletion, "x" for exchange and "m" for no operation (matching).


Gazetteers (predefined lists of NEs) and annotated corpora are not required by ASem-iNER.

aSemiNER is a bootstrapping algorithm that takes a few examples of a particular NE type as input and iteratively induces and learns patterns, which are used to extract more examples.


The lists of trigger nouns are semiautomatically extracted from randomly selected Arabic Wikipedia articles.

we extract nouns that appear most frequently before or after the NE and stored them as trigger nouns.


In cotraining for domain adaptation of parsers, we follow Goldman and Zhou (2000) in assuming two different parsers rather than two different feature sets.

the different views come from two parsers built on different parsing algorithms, i.e., with different biases.


We focused on the authorship attribution on medical forums where the authors may post sensitive PHI, e.g., problems with conception.

we worked with data from IVF.ca, an infertility on-line community created by prospective, existing and past IVF (In Vitro Fertilization) patients.


Hence, the texts are more complex in differentiation between the authors.

: a) the posts have a unified content (i.e., all posts are about infertility treatment); b) the same gender of authors (i.e., participants are overwhelmingly women); c) a small age range (most authors are 35-40 years old); d) the same geographic location (most are Canadians and a few USA); e) the same time of posting (2008 -2012).


Comparing the results of the classification using the initial lexicon and using an extended lexicon, the impact of a complete controlled vocabulary becomes apparent.

the completeness of the lexicon contributes to the correct classification of sentences describing pathological findings.


This indicates that while our Recogniser % of query texts % of queries HeidelTime 2.90 1.97 NTE gazetteer 0.06 0.14 Table 3: Temporal intent indicator prevalence in a web search query log approaches do not identify too many non-timexes as being timexes, further work is called for at improving the range of named timexes they recognise.

the temporal expressions used in the TimeBank and AQUAINT corpora have a very small overlap with the named temporal expressions we identified.


Accordingly, our semantic classes had to be tightly coupled with question words.

the way users may ask, determines the granularity of the classification scheme.


The sense pairs (250 positive and 250 negative pairs) were uniformly sampled from sense clusters obtained using the same automatic method described above, excluding the sense pairs that were included in the automatically created datasets.

there is no overlap between the 500 sense pairs in the manually annotated datasets, and the 6,106 (5,000) sense pairs in the automatically created datasets.


We see Norvig's simplistic adaptation of Google's algorithm for spell checking the same way as, for example, the majority class baseline is used in text classification.

the minimum expectable performance that an algorithm should achieve.


on the typology of linguistic features which could be reliably extracted from automatically analysed texts with particular attention to the potential impact of achieved results on two classification tasks.

we identified the set of linguistic features characterizing classes of documents, based on their textual genre or the type of audience they target: to put it in van Halteren words (van Halteren, 2004), we carried out "linguistic profiling" of texts selected as representative of different genres and/or readability levels.


According to the most recent literature on readability, the degree of readability appears to be, at least to some extent, connected to the textual genre of the document under evaluation (Kate, 2010;Štajner, 2012;Dell'Orletta et al., 2012): linguistic features correlated with readability are also genre dependent.

the results achieved in this case study are in line with those obtained by (Sheehan, 2013) who demonstrated that, when genre effects are ignored, readability scores for informational texts (e.g.


As a reference resource we took the Basic Italian Vocabulary by De Mauro 2000, including a list of 7000 words highly familiar to native speakers of Italian.

we calculated two different features corresponding to: i) the percentage of all unique words (types) on this reference list (calculated on a per-lemma basis); ii) the internal distribution of the occurring basic Italian vocabulary words into the usage classification classes of 'fundamental words' (very frequent words), 'high usage words' (frequent words) and 'high availability words' (relatively lower frequency words referring to everyday life).


These corpora (detailed in Table 1) are internally subdivided into two different sets, according to the expected target audience.

the journalistic corpus is articulated into a newspaper corpus, La Repubblica, and an easy-to-read newspaper corpus, Due Parole, which was specifically written by linguists expert in text simplification using a controlled language for an audience of adults with a rudimentary literacy level or with mild intellectual disabilities (Piemontese, 1996).


For what concerns the Literature genre, we focused on one of the three major literary genres, namely fictional prose.

the corpus of Italian literary texts explored here is subdivided into two different sub-corpora, constituted by adult and children literature respectively.


Apart from the obvious technical generalization of the likelihood function the weights could on aver-age be interpreted as a modification of the training set by adding new examples with intensity w(i) while keeping the attributes and the classes (x(i), y(i)).

for w(i) < 1 the ith example is deleted with probability 1 − w(i).


In this case, RN − 1 is 1 because α 0 × C score for the chunk "a patient" is firstly calculated and α 1 × C score for the chunk "doctor" is secondly calculated.

α is used as the parameter for the penalty when the appearance order of chunks between reference and candidate is different.


The value of weight is 0.3896 (=( 1.0 log(20+20) ) 2.0 ) when m and n are respectively 20.

the weight of non-matched words decreases in short sentences adding the large value (e.g., 1.2261) of weight to the matching words (i.e., RN −1 i=0 α i × C score in Eqs.


In this section, we investigate the predictive power of the features used in a microevaluation level.

we make a summary level evaluation in which we take each summary score in a separate entry.


However, this structure would partly be FWO-affected.

the priority of node appearance in a tree is dependent on their orders in the sentence.


In feature selection, we performed a two-stage analysis on the feature set by measuring the accuracies of both single-feature-included and single-feature-excluded models for each feature.

two K Simple kernels were trained on two feature sets.


It defines <pos> to be the value "<table "<prev pos>" "<prev prev pos>" >".

to determine pos use the lookup table table, indexed with the pos of the previous two words.


In order to observe the effect of (explicit) discourse connective senses and their argument spans, we use conditional random fields with an extended set of features from shallow discourse structure by Ghosh (2012) on the top of the baseline features.

we use one of the four explicit discourse connective senses (viz.


Thus, we assume that people not only choose attributes on the basis of some intrinsic properties, such as salience, but also on the basis of their contribution to the identification of the intended referent.

an attribute is more likely to be chosen if it alone allows the identification rather than in a situation where several objects share the value of this attribute.


Within these aggregations, we have examined several attribute-value pairs in the set of results, as to whether their uses qualify for one of the regularities as defined in the previous section.

we have tested the role of the most salient attributes color, wearing glasses, and beardedness, we have made a comparison between size and orientation of pieces of furniture, and we have tested the role of some values of a person's hair (color, no hair).


In addition to the increased quantity of data, it is necessary to make more fine-grained distinctions of salience categories than we did so far.

a context-dependent aspect appears to be useful, which would allow one to distinguish attributes that stand out in terms of salience (such as color in the furniture domain) from similarly salient attributes -there exist several in the TUNA corpus (such as wearing glasses and beardedness).


To see if our features are still effective in a class-imbalanced environment, we created a third dataset with a 90% negative, 10% positive distribution for both the training and test sets 6 .

we used the first dataset and then added an extra 8 negative instances for each positive instance.


However, the errors introduced in the preceding steps are carried over to subsequent steps after phrase formation.

the quality of proto-synsets is only as good as the quality of word alignment, not worse.


Each of written operators refers to one semantic relation.

each semantic relation is described by one or by many WCCL operators.


The current work extends this previous work by more closely examining the data given as input for training the parser.


at the 0.05/0.01 level, testing the 4 perplexity models as compared to EWT+WSJSent) domain data set that are similar to the sentences in the target domain rather than just selecting a portion of consecutive sentences.

can we identify sentences from the WSJ that will have the best impact on a parser for web data?


Low Perplexity close to the median (Med-LowP): Here, we investigate the effect of choosing sentences that are less similar to the test sentences: we select the same number of sentences as with LowP, but this time from the median down.

the sentences with the lowest perplexity, i.e., the most similar sentences, are excluded.


also Carrillo de Albornoz et al., 2011).

from a reader's perspective, there is a discrepancy between what the reviewer expresses with the numerical rating and what is expressed in text.


The first corpus included 3,000 sentences, which were manually changed in such a way that the structure of "S PP O V" was held.

the common property of the sentences in this corpus was that the order of words were artificially fixed (i.e., they were not free in order).


Verbs (argument structure distinctions): Merlo and Stevenson (2001) consider thematic relations to be crucial for verb classification, and hence propose a classification of verbs that is coarser than the one proposed by Levin and considered to be appropriate for numerous language engineering tasks.

the relevant features to be considered are causativity, animacy, the passive vs. active voice, and the use of pastparticiple vs. simple past.


Finally, while content words are the dimensions required by the semantic space of nouns to better picture them, verbs require to also consider function words.

to distinguish negative from positive verbs (in the sense of sentiment analysis) a major role is played by grammatical words like coordination, pronouns, and prepositions; whereas adverbs seems to be more relevant for recognizing downward entailing verbs.


If E={w 1, w 2 ,…,w i ,…,w n } is a sentence and w i is a real-word error in the sentence, it could appear in several confusion sets, thus, there are several headwords as candidates for the suspicious word.

each headword that has w i in its confusion set can be suggested as the correct word.


Graph-based (GB) learning has been intensively studied in the last ten years (Zhu et al., 2003;Joachims, 2003;Talukdar and Crammer, 2009; and applied to many NLP tasks.

in the field of sentiment analysis GB models have been employed for sentiment classification (Pang and Lee, 2004;Goldberg and Zhu, 2006;Wu et al., 2009), automatic building of sentiment lexicons (Hassan and Radev, 2010;Xu et al., 2010), cross-lingual sentiment analysis (Scheible et al., 2010) and social media analysis (Speriosu et al., 2011).


First, we attempt to formalise and unify the research on GB approaches in the field of sentiment analysis.

we conduct a comparison between LP and its variants and study the impact of different graph structures and parameter values on algorithm performance.


Parameter α controls the weight of the GB solution compared to the initial predictions.

α close to 0 gives more importance to the initial predictions whilst high values of α prioritise the GB solution.


Goldberg and Zhu (2006) also used in-domain labeled data to approximate sentiment similarity for semi-supervised sentiment classification.

they constructed a vector representation based on document words.


For large discrepancies between source and target data GB-SSL can help to achieve good results with a reasonably small amount of labeled data.

even 500 labeled examples are enough to ensure performance within a 95% confidence interval of the in-domain accuracy.


Because a word can belong to several synsets, in order to favor the most frequent senses as opposed to the infrequent ones, the weight of this category is modulated by a factor β.

we use (4 × α + β), where β is computed as the ratio of the frequency of the sense number in WordNet over the maximum number of senses for that word.


We evaluated the approach with several benchmarks and achieved interesting results, often among the best systems.

the approach achieved a Pearson correlation of 0.93 with the M&C human ratings, a Spearman correlation of 0.50 on the Word Similarity353 data set, an accuracy of 84% on the ESL synonym test, and an accuracy of 91.25% on the TOEFL synonym test.


Finally, some important issues in computing lexicon based semantic similarity in general must still be addressed.

all words that are related to another word by the same path will have the same semantic relatedness.


Moreover, Fscore's optimum at synset 6 is independent from the presence of personal ontology terms.

it holds in both cases of personal health information extraction -with the PO terms and without them.


One consequence of the various types of restrictions that can be seen in various researchers' conceptions of the notion of sublanguage is that various components of the language should tend towards finiteness.

if we examine sufficient quantities of a sample of the language, we should observe an eventual slowing or stoppage of growth in new items in that component of the language.


We applied the method on a custom dataset from Press Association, for named entity disambiguation.

we recognized Locations from negative entities.


• Precision: The proportion of the correctly detected errors.

how many errors that the classifier detects were actually correct   According to Figure 5 although the MLP has a highest precision (100%), its recall is slightly lower than the C4.5 classifier.


Besides attaining merit from a purely linguistic point of view, providing technologies for Persian text analysis has also business implications in the regions where the language remains a preferred working language.

sentiment analysis has a high poten-tial in providing insights for several Persian online communities and social media.


Although it is still not as precise as the ML-based approach described in (Basiri et al., 2014), this compares fairly well with related work and the experiments confirm that there is value in our approach.

an acceptably accurate lexicon-based approach can be used to bootstrap an ML-based system that does not require a large training set to start achieving results.


In addition we also intend to improve the grammar rules to cover more of the exceptions and characteristics of the Persian language.

we want to address rules centered around notorious Persian con-junctions, such as 'but and 'although.


As recognizing good questions can improve the CQA services and the user's experience, the current study focuses on question quality instead.

we predict question quality and investigate the features which influence it.


For number of answers, a Chi-square test is used to show that the counts of parts of speech differ significantly between high and low quality questions (χ 2 = 37.362, df = 3, p = 0.01).

the percentage of nouns is higher in the groups of terms predicting low question quality -65.04%.


As a consequence we were able to retrieve by the Twitter API the text of only a subset of the Tweets included in the original SENTIPOLC dataset.

our training set included 3998 Tweets (while the original dataset included 4513).


the previous section, not all of our features are effective for the SENTIPOLC Tasks.

in Polarity and Irony Tasks the features with biggest impact on the classification accuracy resulted to be the domain dependent ones.


TT is not a motif because it always occurs inside an occurrence of ATTI.

its right-context is always I and its left-context A.


REDUCTIO AD ABSURDUM -Let assume that R n ⊂ R n−1 .


As defined before, motifs are a condensed way to represent all substrings of a corpus.

for a fixed value of n, the set of motifs of size n is a subset of all the character n-grams of a corpus (as well with variable length substrings: motifs with length in [min, max] or character [min, max]-grams).


From a practical point of view in software application (real scenario) for the algorithms we also do not have the assurance that all documents given as examples of an author, have actually been written by the author in question.

it is possible that some samples were drafted by someone else.


This result shows that traditonal text summarization techniques, which only consider text representativeness, may not be able to summarize opinions from online forums very well.

we hypothesize that two important factors should be considered for summarizing online discussions.


Text summarization in social media: Recently with the explosion of social media, there has been much work on summarizing social media content.

much attention has been paid to Twitter summarization (Chua and Asur, 2013;Meng et al., 2012).


Knowing what features are useful can help us design better summarization methods for forum opinion summarization problem.

since all our feature values are numerical, we perform unpaired Student's t-test on each feature.


Although we hypothesize that the features we have identified are useful, it turns out that not all of them have a statistically significant impact on whether the sentence is a good summary sentence.

we find that sentence length has a positive impact.


This suggests that barbecue is only used in this meaning, and that the subject can only be a Human, while the object can only be of type Food.

one can unambiguously collect Food instances by looking at the nouns that occur as objects of the verb barbecue.


This work is the first to use semantic preferences from PDEV for ontology population from the web, therefore it is still work in progress.

important is to investigate the best use of the ontology structure as part of pattern extraction algorithms.


But the enrichment problem still remains.

these lexicons describe the syntactic behaviours knowledge linked to lexical entries but not to their meanings.